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The impact of an online course on agreement rates of the certainty of evidence assessment using Grading of Recommendations, Assessment, Development, and Evaluation Approach: a before-and-after study

2024· article· en· W4399303408 on OpenAlexaff
Gilson Pires Dorneles, Cinara Stein, Cintia Laura Pereira de Araújo, Suena Medeiros Parahiba, Bruna da Rosa, Débora Dalmas Gräf, Karlyse Claudino Belli, John Basmaji, Marta da Cunha Lobo Souto Maior, Ávila Teixeira Vidal, Verônica Colpani, Maicon Falavigna

Bibliographic record

VenueJournal of Clinical Epidemiology · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsWestern University
FundersMinistério da SaúdeHospital Moinhos de Vento
KeywordsCertaintyMedicineAgreementStatisticsPsychologyMathematicsLinguistics

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.313
metaresearch head score (Gemma)0.675
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score0.847

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3130.675
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0070.016
Bibliometrics0.0030.004
Science and technology studies0.0020.002
Scholarly communication0.0070.010
Open science0.0030.005
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0070.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.955
GPT teacher head0.752
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNon-randomized trial
DomainMethods
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2024
Admission routes1
Has abstractno

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